Scale-invariant unconstrained online learning

23 Aug 2017 Wojciech Kotłowski

We consider a variant of online convex optimization in which both the instances (input vectors) and the comparator (weight vector) are unconstrained. We exploit a natural scale invariance symmetry in our unconstrained setting: the predictions of the optimal comparator are invariant under any linear transformation of the instances... (read more)

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